Search tips
Search criteria

Results 1-25 (909)

Clipboard (0)

Select a Filter Below

Year of Publication
more »
1.  Model‐guided combinatorial optimization of complex synthetic gene networks 
Molecular Systems Biology  2016;12(12):899.
Constructing gene circuits that satisfy quantitative performance criteria has been a long‐standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three‐gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high‐throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model‐guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.
PMCID: PMC5199127  PMID: 28031353
library screening; miRNA sensor; modeling; synthetic gene circuit; Network Biology; Synthetic Biology & Biotechnology
2.  Proteomics reveals the effects of sustained weight loss on the human plasma proteome 
Molecular Systems Biology  2016;12(12):901.
Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P < 10−13), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases.
PMCID: PMC5199119  PMID: 28007936
diabetes; mass spectrometry; metabolic syndrome; obesity; plasma proteome profiling; Metabolism; Post-translational Modifications, Proteolysis & Proteomics; Systems Medicine
3.  Frequency doubling in the cyanobacterial circadian clock 
Molecular Systems Biology  2016;12(12):896.
Organisms use circadian clocks to generate 24‐h rhythms in gene expression. However, the clock can interact with other pathways to generate shorter period oscillations. It remains unclear how these different frequencies are generated. Here, we examine this problem by studying the coupling of the clock to the alternative sigma factor sigC in the cyanobacterium Synechococcus elongatus. Using single‐cell microscopy, we find that psbAI, a key photosynthesis gene regulated by both sigC and the clock, is activated with two peaks of gene expression every circadian cycle under constant low light. This two‐peak oscillation is dependent on sigC, without which psbAI rhythms revert to one oscillatory peak per day. We also observe two circadian peaks of elongation rate, which are dependent on sigC, suggesting a role for the frequency doubling in modulating growth. We propose that the two‐peak rhythm in psbAI expression is generated by an incoherent feedforward loop between the clock, sigC and psbAI. Modelling and experiments suggest that this could be a general network motif to allow frequency doubling of outputs.
PMCID: PMC5199125  PMID: 28007935
circadian clock; cyanobacteria; mathematical modelling; network motifs; single‐cell time‐lapse microscopy; Network Biology; Quantitative Biology & Dynamical Systems
4.  Non‐genetic diversity modulates population performance 
Molecular Systems Biology  2016;12(12):895.
Biological functions are typically performed by groups of cells that express predominantly the same genes, yet display a continuum of phenotypes. While it is known how one genotype can generate such non‐genetic diversity, it remains unclear how different phenotypes contribute to the performance of biological function at the population level. We developed a microfluidic device to simultaneously measure the phenotype and chemotactic performance of tens of thousands of individual, freely swimming Escherichia coli as they climbed a gradient of attractant. We discovered that spatial structure spontaneously emerged from initially well‐mixed wild‐type populations due to non‐genetic diversity. By manipulating the expression of key chemotaxis proteins, we established a causal relationship between protein expression, non‐genetic diversity, and performance that was theoretically predicted. This approach generated a complete phenotype‐to‐performance map, in which we found a nonlinear regime. We used this map to demonstrate how changing the shape of a phenotypic distribution can have as large of an effect on collective performance as changing the mean phenotype, suggesting that selection could act on both during the process of adaptation.
PMCID: PMC5199129  PMID: 27994041
cellular motility; chemotaxis; Jensen's inequality; non‐genetic diversity; nonlinear systems; Microbiology, Virology & Host Pathogen Interaction; Quantitative Biology & Dynamical Systems; Signal Transduction
5.  Diverse mechanisms of metaeffector activity in an intracellular bacterial pathogen, Legionella pneumophila  
Molecular Systems Biology  2016;12(12):893.
Pathogens deliver complex arsenals of translocated effector proteins to host cells during infection, but the extent to which these proteins are regulated once inside the eukaryotic cell remains poorly defined. Among all bacterial pathogens, Legionella pneumophila maintains the largest known set of translocated substrates, delivering over 300 proteins to the host cell via its Type IVB, Icm/Dot translocation system. Backed by a few notable examples of effector–effector regulation in L. pneumophila, we sought to define the extent of this phenomenon through a systematic analysis of effector–effector functional interaction. We used Saccharomyces cerevisiae, an established proxy for the eukaryotic host, to query > 108,000 pairwise genetic interactions between two compatible expression libraries of ~330 L. pneumophila‐translocated substrates. While capturing all known examples of effector–effector suppression, we identify fourteen novel translocated substrates that suppress the activity of other bacterial effectors and one pair with synergistic activities. In at least nine instances, this regulation is direct—a hallmark of an emerging class of proteins called metaeffectors, or “effectors of effectors”. Through detailed structural and functional analysis, we show that metaeffector activity derives from a diverse range of mechanisms, shapes evolution, and can be used to reveal important aspects of each cognate effector's function. Metaeffectors, along with other, indirect, forms of effector–effector modulation, may be a common feature of many intracellular pathogens—with unrealized potential to inform our understanding of how pathogens regulate their interactions with the host cell.
PMCID: PMC5199130  PMID: 27986836
effector; genetic interaction; Legionella; metaeffector; structure‐function; Chromatin, Epigenetics, Genomics & Functional Genomics; Genetics, Gene Therapy & Genetic Disease; Microbiology, Virology & Host Pathogen Interaction
6.  Distinct cellular states determine calcium signaling response 
Molecular Systems Biology  2016;12(12):894.
The heterogeneity in mammalian cells signaling response is largely a result of pre‐existing cell‐to‐cell variability. It is unknown whether cell‐to‐cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single‐cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single‐cell level.
PMCID: PMC5199124  PMID: 27979909
calcium signaling; cell states; cellular heterogeneity; single‐cell biology; Quantitative Biology & Dynamical Systems; Signal Transduction
7.  Disentangling genetic and epigenetic determinants of ultrafast adaptation 
Molecular Systems Biology  2016;12(12):892.
A major rationale for the advocacy of epigenetically mediated adaptive responses is that they facilitate faster adaptation to environmental challenges. This motivated us to develop a theoretical–experimental framework for disclosing the presence of such adaptation‐speeding mechanisms in an experimental evolution setting circumventing the need for pursuing costly mutation–accumulation experiments. To this end, we exposed clonal populations of budding yeast to a whole range of stressors. By growth phenotyping, we found that almost complete adaptation to arsenic emerged after a few mitotic cell divisions without involving any phenotypic plasticity. Causative mutations were identified by deep sequencing of the arsenic‐adapted populations and reconstructed for validation. Mutation effects on growth phenotypes, and the associated mutational target sizes were quantified and embedded in data‐driven individual‐based evolutionary population models. We found that the experimentally observed homogeneity of adaptation speed and heterogeneity of molecular solutions could only be accounted for if the mutation rate had been near estimates of the basal mutation rate. The ultrafast adaptation could be fully explained by extensive positive pleiotropy such that all beneficial mutations dramatically enhanced multiple fitness components in concert. As our approach can be exploited across a range of model organisms exposed to a variety of environmental challenges, it may be used for determining the importance of epigenetic adaptation‐speeding mechanisms in general.
PMCID: PMC5199126  PMID: 27979908
adaptation; epigenetics; evolution; modelling; population genetics; Evolution; Genome-Scale & Integrative Biology
8.  Exploiting native forces to capture chromosome conformation in mammalian cell nuclei 
Molecular Systems Biology  2016;12(12):891.
Mammalian interphase chromosomes fold into a multitude of loops to fit the confines of cell nuclei, and looping is tightly linked to regulated function. Chromosome conformation capture (3C) technology has significantly advanced our understanding of this structure‐to‐function relationship. However, all 3C‐based methods rely on chemical cross‐linking to stabilize spatial interactions. This step remains a “black box” as regards the biases it may introduce, and some discrepancies between microscopy and 3C studies have now been reported. To address these concerns, we developed “i3C”, a novel approach for capturing spatial interactions without a need for cross‐linking. We apply i3C to intact nuclei of living cells and exploit native forces that stabilize chromatin folding. Using different cell types and loci, computational modeling, and a methylation‐based orthogonal validation method, “TALE‐iD”, we show that native interactions resemble cross‐linked ones, but display improved signal‐to‐noise ratios and are more focal on regulatory elements and CTCF sites, while strictly abiding to topologically associating domain restrictions.
PMCID: PMC5199122  PMID: 27940490
chromatin looping; chromosome conformation capture; cross‐linking; nuclear compartments; nuclear organization; Chromatin, Epigenetics, Genomics & Functional Genomics; Genome-Scale & Integrative Biology; Methods & Resources
9.  Capturing native interactions: intrinsic methods to study chromatin conformation 
Molecular Systems Biology  2016;12(12):897.
The 3D organization of chromatin controls gene expression through spatial interactions between genomic loci. FISH and 3C‐based methods that are commonly used to study chromatin organization utilize chemical crosslinking, a step that may introduce biases in detectable chromatin interactions. In their recent study, Papantonis and colleagues (Brant et al, 2016) developed alternative new methods of detecting chromatin contacts without the use of chemical crosslinking agents. These tools increase the resolution and confidence at which interactions can be identified, and may be informative for chromatin interaction dynamics.
PMCID: PMC5199123  PMID: 27940491
Chromatin, Epigenetics, Genomics & Functional Genomics; Genome-Scale & Integrative Biology; Methods & Resources
10.  Global analysis of regulatory divergence in the evolution of mouse alternative polyadenylation 
Molecular Systems Biology  2016;12(12):890.
Alternative polyadenylation (APA), which is regulated by both cis‐elements and trans‐factors, plays an important role in post‐transcriptional regulation of eukaryotic gene expression. However, comparing to the extensively studied transcription and alternative splicing, the extent of APA divergence during evolution and the relative cis‐ and trans‐contribution remain largely unexplored. To directly address these questions for the first time in mammals, by using deep sequencing‐based methods, we measured APA divergence between C57BL/6J and SPRET/EiJ mouse strains as well as allele‐specific APA pattern in their F1 hybrids. Among the 24,721 polyadenylation sites (pAs) from 7,271 genes expressing multiple pAs, we identified 3,747 pAs showing significant divergence between the two strains. After integrating the allele‐specific data from F1 hybrids, we demonstrated that these events could be predominately attributed to cis‐regulatory effects. Further systematic sequence analysis of the regions in proximity to cis‐divergent pAs revealed that the local RNA secondary structure and a poly(U) tract in the upstream region could negatively modulate the pAs usage.
PMCID: PMC5199128  PMID: 27932516
alternative polyadenylation; evolution; regulatory divergence; Chromatin, Epigenetics, Genomics & Functional Genomics; Genome-Scale & Integrative Biology; Transcription
11.  An atlas of human kinase regulation 
Molecular Systems Biology  2016;12(12):888.
The coordinated regulation of protein kinases is a rapid mechanism that integrates diverse cues and swiftly determines appropriate cellular responses. However, our understanding of cellular decision‐making has been limited by the small number of simultaneously monitored phospho‐regulatory events. Here, we have estimated changes in activity in 215 human kinases in 399 conditions derived from a large compilation of phosphopeptide quantifications. This atlas identifies commonly regulated kinases as those that are central in the signaling network and defines the logic relationships between kinase pairs. Co‐regulation along the conditions predicts kinase–complex and kinase–substrate associations. Additionally, the kinase regulation profile acts as a molecular fingerprint to identify related and opposing signaling states. Using this atlas, we identified essential mediators of stem cell differentiation, modulators of Salmonella infection, and new targets of AKT1. This provides a global view of human phosphorylation‐based signaling and the necessary context to better understand kinase‐driven decision‐making.
PMCID: PMC5199121  PMID: 27909043
cell fate; human; kinase activity; phosphoproteomics; signaling; Genome-Scale & Integrative Biology; Post-translational Modifications, Proteolysis & Proteomics; Signal Transduction
12.  Single‐cell sequencing maps gene expression to mutational phylogenies in PDGF‐ and EGF‐driven gliomas 
Molecular Systems Biology  2016;12(11):889.
Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor. Epidermal growth factor (EGF) and platelet‐derived growth factor (PDGF) receptors are frequently amplified and/or possess gain‐of‐function mutations in GBM. However, clinical trials of tyrosine‐kinase inhibitors have shown disappointing efficacy, in part due to intra‐tumor heterogeneity. To assess the effect of clonal heterogeneity on gene expression, we derived an approach to map single‐cell expression profiles to sequentially acquired mutations identified from exome sequencing. Using 288 single cells, we constructed high‐resolution phylogenies of EGF‐driven and PDGF‐driven GBMs, modeling transcriptional kinetics during tumor evolution. Descending the phylogenetic tree of a PDGF‐driven tumor corresponded to a progressive induction of an oligodendrocyte progenitor‐like cell type, expressing pro‐angiogenic factors. In contrast, phylogenetic analysis of an EGFR‐amplified tumor showed an up‐regulation of pro‐invasive genes. An in‐frame deletion in a specific dimerization domain of PDGF receptor correlates with an up‐regulation of growth pathways in a proneural GBM and enhances proliferation when ectopically expressed in glioma cell lines. In‐frame deletions in this domain are frequent in public GBM data.
PMCID: PMC5147052  PMID: 27888226
copy‐number variation; glioblastoma; PDGFRA; single‐cell RNA‐sequencing; tumor phylogeny; Cancer; Chromatin, Epigenetics, Genomics & Functional Genomics; Genome-Scale & Integrative Biology
13.  Unlocking the chromatin code by deciphering protein–DNA interactions 
Molecular Systems Biology  2016;12(11):887.
Characterizing the composition of protein complexes bound to different genomic loci is essential for advancing our mechanistic understanding of transcriptional regulation. In their recent study, Krijgsveld and colleagues (Rafiee et al, 2016) report ChIP‐SICAP, a powerful tool for deciphering the chromatin proteome by combining chromatin immunoprecipitation, selective isolation of chromatin‐associated proteins and mass spectrometry.
PMCID: PMC5114617  PMID: 27837035
Methods & Resources; Post-translational Modifications, Proteolysis & Proteomics; Transcription
14.  Dynamical compensation in physiological circuits 
Molecular Systems Biology  2016;12(11):886.
Biological systems can maintain constant steady‐state output despite variation in biochemical parameters, a property known as exact adaptation. Exact adaptation is achieved using integral feedback, an engineering strategy that ensures that the output of a system robustly tracks its desired value. However, it is unclear how physiological circuits also keep their output dynamics precise—including the amplitude and response time to a changing input. Such robustness is crucial for endocrine and neuronal homeostatic circuits because they need to provide a precise dynamic response in the face of wide variation in the physiological parameters of their target tissues; how such circuits compensate their dynamics for unavoidable natural fluctuations in parameters is unknown. Here, we present a design principle that provides the desired robustness, which we call dynamical compensation (DC). We present a class of circuits that show DC by means of a nonlinear feedback loop in which the regulated variable controls the functional mass of the controlling endocrine or neuronal tissue. This mechanism applies to the control of blood glucose by insulin and explains several experimental observations on insulin resistance. We provide evidence that this mechanism may also explain compensation and organ size control in other physiological circuits.
PMCID: PMC5147051  PMID: 27875241
calcium homeostasis; dynamical compensation; endocrine circuits; glucose homeostasis; mathematical models of disease; Metabolism; Molecular Biology of Disease; Quantitative Biology & Dynamical Systems
15.  A gene‐centered C. elegans protein–DNA interaction network provides a framework for functional predictions 
Molecular Systems Biology  2016;12(10):884.
Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein–DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene‐centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two‐thirds of which are novel, as well as integrate gene expression, protein–protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.
PMCID: PMC5081483  PMID: 27777270
C. elegans; gene regulation; protein–DNA interaction network; transcription factors; yeast one‐hybrid assays; Genome-Scale & Integrative Biology; Network Biology; Transcription
16.  Quantifying gene expression: the importance of being subtle 
Molecular Systems Biology  2016;12(10):885.
Gene expression is regulated at both the mRNA and protein level through on‐off switches and fine‐tuned control. In their recent study, Edfors et al (2016) use highly accurate, targeted proteomics methods and examine to what extent the amount of protein produced per mRNA transcript varies across different tissues. They find that the bulk part of protein concentrations is set at a per‐gene level: This relationship, the protein/mRNA ratio, is constant across cell types and tissues, but varies by several orders of magnitude across genes.
PMCID: PMC5081482  PMID: 27951528
Genome-Scale & Integrative Biology; Post-translational Modifications, Proteolysis & Proteomics; Transcription
17.  Gene‐specific correlation of RNA and protein levels in human cells and tissues 
Molecular Systems Biology  2016;12(10):883.
An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non‐secreted proteins based on parallel reaction monitoring to measure, at steady‐state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene‐specific RNA‐to‐protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.
PMCID: PMC5081484  PMID: 27951527
gene expression; protein quantification; targeted proteomics; transcriptomics; Genome-Scale & Integrative Biology; Post-translational Modifications, Proteolysis & Proteomics; Transcription
19.  Deep learning for computational biology 
Molecular Systems Biology  2016;12(7):878.
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.
PMCID: PMC4965871  PMID: 27474269
cellular imaging; computational biology; deep learning; machine learning; regulatory genomics; Computational Biology
20.  Negative frequency‐dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population 
Molecular Systems Biology  2016;12(8):877.
Genetically identical cells in microbial populations often exhibit a remarkable degree of phenotypic heterogeneity even in homogenous environments. Such heterogeneity is commonly thought to represent a bet‐hedging strategy against environmental uncertainty. However, evolutionary game theory predicts that phenotypic heterogeneity may also be a response to negative frequency‐dependent interactions that favor rare phenotypes over common ones. Here we provide experimental evidence for this alternative explanation in the context of the well‐studied yeast GAL network. In an environment containing the two sugars glucose and galactose, the yeast GAL network displays stochastic bimodal activation. We show that in this mixed sugar environment, GAL‐ON and GAL‐OFF phenotypes can each invade the opposite phenotype when rare and that there exists a resulting stable mix of phenotypes. Consistent with theoretical predictions, the resulting stable mix of phenotypes is not necessarily optimal for population growth. We find that the wild‐type mixed strategist GAL network can invade populations of both pure strategists while remaining uninvasible by either. Lastly, using laboratory evolution we show that this mixed resource environment can directly drive the de novo evolution of clonal phenotypic heterogeneity from a pure strategist population. Taken together, our results provide experimental evidence that negative frequency‐dependent interactions can underlie the phenotypic heterogeneity found in clonal microbial populations.
PMCID: PMC5119493  PMID: 27487817
ecology; evolution; frequency dependence; phenotypic heterogeneity; stochastic gene expression; Evolution; Microbiology, Virology & Host Pathogen Interaction; Quantitative Biology & Dynamical Systems
21.  Frequency‐dependent selection: a diversifying force in microbial populations 
Molecular Systems Biology  2016;12(8):880.
The benefits of “bet‐hedging” strategies have been assumed to be the main cause of phenotypic diversity in biological populations. However, in their recent work, Healey et al (2016) provide experimental support for negative frequency‐dependent selection (NFDS) as an alternative driving force of diversity. NFDS favors rare phenotypes over common ones, resulting in an evolutionarily stable mixture of phenotypes that is not necessarily optimal for population growth.
PMCID: PMC5119495  PMID: 27487818
Evolution; Microbiology, Virology & Host Pathogen Interaction; Quantitative Biology & Dynamical Systems
22.  Parallel reverse genetic screening in mutant human cells using transcriptomics 
Molecular Systems Biology  2016;12(8):879.
Reverse genetic screens have driven gene annotation and target discovery in model organisms. However, many disease‐relevant genotypes and phenotypes cannot be studied in lower organisms. It is therefore essential to overcome technical hurdles associated with large‐scale reverse genetics in human cells. Here, we establish a reverse genetic approach based on highly robust and sensitive multiplexed RNA sequencing of mutant human cells. We conduct 10 parallel screens using a collection of engineered haploid isogenic cell lines with knockouts covering tyrosine kinases and identify known and unexpected effects on signaling pathways. Our study provides proof of concept for a scalable approach to link genotype to phenotype in human cells, which has broad applications. In particular, it clears the way for systematic phenotyping of still poorly characterized human genes and for systematic study of uncharacterized genomic features associated with human disease.
PMCID: PMC5119491  PMID: 27482057
kinases; multiplexed RNA sequencing; parallel screening; reverse genetics; systematic phenotyping; Chromatin, Epigenetics, Genomics & Functional Genomics; Methods & Resources
23.  Deep learning for computational biology 
Molecular Systems Biology  2016;12(7):878.
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.
PMCID: PMC4965871  PMID: 27474269
cellular imaging; computational biology; deep learning; machine learning; regulatory genomics; Computational Biology
24.  Co‐recruitment analysis of the CBL and CBLB signalosomes in primary T cells identifies CD5 as a key regulator of TCR‐induced ubiquitylation 
Molecular Systems Biology  2016;12(7):876.
T‐cell receptor (TCR) signaling is essential for the function of T cells and negatively regulated by the E3 ubiquitin–protein ligases CBL and CBLB. Here, we combined mouse genetics and affinity purification coupled to quantitative mass spectrometry to monitor the dynamics of the CBL and CBLB signaling complexes that assemble in normal T cells over 600 seconds of TCR stimulation. We identify most previously known CBL and CBLB interacting partners, as well as a majority of proteins that have not yet been implicated in those signaling complexes. We exploit correlations in protein association with CBL and CBLB as a function of time of TCR stimulation for predicting the occurrence of direct physical association between them. By combining co‐recruitment analysis with biochemical analysis, we demonstrated that the CD5 transmembrane receptor constitutes a key scaffold for CBL‐ and CBLB‐mediated ubiquitylation following TCR engagement. Our results offer an integrated view of the CBL and CBLB signaling complexes induced by TCR stimulation and provide a molecular basis for their negative regulatory function in normal T cells.
PMCID: PMC4965873  PMID: 27474268
CBL; CBLB; CD5; ubiquitylation; Immunology; Post-translational Modifications, Proteolysis & Proteomics; Signal Transduction
25.  Sensing a revolution 
Molecular Systems Biology  2016;12(4):867.
New fully integrated biosensors that monitor molecular and physiological parameters throughout our bodies are set to revolutionize medicine and personalized healthcare.
PMCID: PMC4848763  PMID: 27118815
Systems Medicine

Results 1-25 (909)